Fingerprint pattern recognition from bifurcations: An alternative approach

  • Authors

    • A. Castañeda-Miranda
    • R. Castañeda-Miranda
    • Victor Castano Centro de Fisica Aplicada y Tecnologia Avanzada, Universidad Nacional Autonoma de Mexico http://orcid.org/0000-0002-2983-5293
    2015-05-31
    https://doi.org/10.14419/jacst.v4i2.4343
  • Fingerprints, Dactiloscopy, Pattern Recognition, Pc-Based Software, Segmentation.
  • A pc-based automatic system for fingerprints recording and classification is described, based on the vector analysis of bifurcations. The system consists of a six-step process: a) acquisition, b) preprocessing, c) fragmentation, d) representation, e) description, and f) recognition. Details of each stage, along with actual examples of fingerprints recognition are provided.

  • References

    1. [1] M. Sayed, Coset decomposition method for storing and decoding fingerprint data, Journal of Advanced Computer Science & Technology, vol. 4, num. 1, pp 6-11, (2015).

      [2] A. Jain and S. Aggarwal, "Multimodal Biometric System: A Survey", International Journal of Applied Science and Advance Technology, vol. 1, no. 1, pp. 58–63, 2012.

      [3] T.H. Nguyen, Y. Wang, R. Li, An improved ridge features extraction algorithm for distorted fingerprints matching, Journal of Information Security and Applications, vol. 18, num. 4, pp. 206-214, 2013. http://dx.doi.org/10.1016/j.jisa.2013.11.001.

      [4] D. Maltoni, D. Maio, A.K. Jain, S. Prabhaker, Handbook of Fingerprint Recognition, Springer-Verlag, London, 2009. http://dx.doi.org/10.1007/978-1-84882-254-2.

      [5] J. Abraham, C. Champod, C. Lennard, C. Roux, Modern statistical models for forensic fingerprint examinations: A critical review, Forensic Science International, vol. 232, num. 1–3, pp. 131–150, 2013. http://dx.doi.org/10.1016/j.forsciint.2013.07.005.

      [6] J.A. Speir, J. Hietpas, and Frequency filtering to suppress background noise in fingerprint evidence: Quantifying the fidelity of digitally enhanced fingerprint images, Forensic Science International, Vol. 242, p94–102, 2014. http://dx.doi.org/10.1016/j.forsciint.2014.06.026.

      [7] A. Ross, A. Jain, J. Reismanb, A hybrid fingerprint matcher, Pattern Recognition, vol. 36, pp 1661- 1663, 2003. http://dx.doi.org/10.1016/S0031-3203(02)00349-7.

      [8] L. Nanni, A. Lumini, A hybrid wavelet based fingerprint matcher, Pattern Recognition, vol. 40, pp. 3146-3151, 2007. http://dx.doi.org/10.1016/j.patcog.2007.02.018.

      [9] C. Neumann, C. Champod, M. Yoo, T. Genessay, G. Langenburg, Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks, Forensic Science International, Vol. 248, pp. 154–171, 2015. http://dx.doi.org/10.1016/j.forsciint.2015.01.007.

      [10] H.C.Lee, R.E. Gaensslen, Advances in Fringerprint Technology, CRC Press,NewYork, 2001.

  • Downloads

  • How to Cite

    Castañeda-Miranda, A., Castañeda-Miranda, R., & Castano, V. (2015). Fingerprint pattern recognition from bifurcations: An alternative approach. Journal of Advanced Computer Science & Technology, 4(2), 220-224. https://doi.org/10.14419/jacst.v4i2.4343